Quantitative Systems Pharmacology Approaches for Immuno-Oncology: Adding Virtual Patients to the Development Paradigm

Clin Pharmacol Ther. 2021 Mar;109(3):605-618. doi: 10.1002/cpt.1987. Epub 2020 Aug 14.

Abstract

Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno-oncology (IO) the aim is to direct the patient's own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD-L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug-development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds' pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.

Publication types

  • Review

MeSH terms

  • Allergy and Immunology*
  • Antineoplastic Combined Chemotherapy Protocols / adverse effects
  • Antineoplastic Combined Chemotherapy Protocols / pharmacokinetics
  • Antineoplastic Combined Chemotherapy Protocols / therapeutic use*
  • Computer Simulation
  • Drug Development*
  • Humans
  • Immune Checkpoint Inhibitors / adverse effects
  • Immune Checkpoint Inhibitors / pharmacokinetics
  • Immune Checkpoint Inhibitors / therapeutic use*
  • Medical Oncology*
  • Models, Immunological
  • Molecular Dynamics Simulation*
  • Molecular Targeted Therapy
  • Neoplasms / drug therapy*
  • Neoplasms / immunology
  • Neoplasms / metabolism
  • Systems Biology*
  • Tumor Microenvironment

Substances

  • Immune Checkpoint Inhibitors